标准必要专利披露对企业市场回报的影响

采用2012~2018年中国沪深A股数字创意产业上市公司数据，对研发投入、知识产权资产和企业价值之间的关系进行理论研究和实证检验，试图解释企业研发投入对企业价值的内在影响机制，并对知识产权资产在企业研发投入和企业价值关系中的中介作用进行实证分析。结果表明，企业通过加大研发资金投入和研发人员投入对提升企业价值均存在明显促进作用；研发投入通过知识产权资产的传导路径间接作用于企业价值，即知识产权资产在研发投入与企业价值两者关系之间起到了部分中介作用。研究结论为管理实践与政策制定提供了有益的启示。

Purpose – This study examines the effects of corporate social responsibility (CSR) disclosure on firm performance measured by Return on Equity (ROE), while examining the moderating role of independent commissioners in strengthen this relationship.Design/methodology/approach – This research uses data obtained from all publicly listed companies on the Indonesia Stock Exchange, comprising 514 firm-year observations from 2018 to 2022. Employing moderated regression analysis model, the study evaluates the direct and moderating effects within the proposed research framework.Findings – The findings reveal that CSR disclosure is positively and significantly related to the firm performance. In addition, independent commissioners are shown to strengthen the relationship, where more independent and objective supervision increases the effectiveness of CSR and attracts investor confidence.Research limitations/implications – This study aggregates CSR disclosure without differentiating its parts and does not account for the features of independent commissioners, such as knowledge or tenure. Future studies should explore these dimensions and conduct comparative or longitudinal studies to enhance the understanding of CSR's impact on financial performance.Practical implications – This study provides guidance for company management to improve CSR strategies by enhancing the oversight quality of independent commissioners. The findings also suggest that policymakers and professional institutions should focus on strengthening the competence and accountability of board members through evaluation frameworks and training programs, to ensure effective governance in CSR practices and long-term firm performance.Originality/value – This study offers a new perspective by examining the moderating role of independent commissioners in the CSR to financial performance relationship in Indonesia, using a more detailed CSR disclosure measure based on the GRI 2021 framework. It provides practical and academic insights into governance and sustainability in emerging markets.

Examining the effect of quantity and quality of sustainability disclosure on innate and discretionary earnings quality is the purpose of this research. the GRI G4 index was used to measure The sustainability disclosure quantity, while the report form, the adherence level and external statements was used to measure the quality of sustainability disclosure. A modified Jones Model uses to measure Innate and discretionary earnings quality as the dependent variable. Return on assets, leverage, net operating assets, and operating cycle were used as control variables. The research sample consisted of 10 main sector companies, namely the agriculture and mining sub-sectors, which are listed in the Indonesia Stock Exchange in 2014-2018. purposive sampling method was used in this research. The technique of analysis in this research is multiple linear regression analysis. As results, innate earnings quality was significantly influenced by sustainability disclosure quantity while discretionary earnings quality was not significantly influenced by sustainability disclosure quantity. Moreover, both innate earnings quality and discretionary earnings quality was significanty influenced by the quality of sustainability disclosure.

Companies carrying out CSR activities can be grouped into three motives, such as: financial motive, ethical motive and altruistic motive. These three motives are the foundation of the company in planning their CSR activities each year. Each motive course has a purpose that has a good impact on the economic and social aspects of the company. A good corporate image ultimately gained public’s trust and will have a positive effect on the financial side of the company and the company's stock.This research will focus on CSR disclosure (CSD) based on company’s motive and check its effect on company's financial performance based on market measurement, seen from investor reaction proxied with stock return. This study uses 56 company annual reports from 2013 to 2016, listed in the "Indonesia Most Trusted Companies Awards" which are fully published in 2014 until 2017 by SWA Magazine.The method of statistical analysis in this study using moderated regression analysis, where independent variables of corporate social disclosure (CSD) using financial, ethical and altruistic motives. While the dependent variable in the form of Corporate Financial Performance (CFP) based on market measurements proxied through stock return.The result of the research shows that corporate social disclosure (CSD) based on financial motive gives effect to stock return, while CSD with ethic motive and altruistic motive can’t provide sufficient evidence to influence the rate of return stock. Keywords: CSD, CFP, CSR, CSR Motive

This study aims to analyze the effect of capital structure and Corporate Social Responsibility (CSR) disclosure on the financial performance of industrial sector C IDX-IC companies listed on the Indonesia Stock Exchange during 2021–2023. Capital structure is proxied by the ratio of long-term debt to equity, while financial performance is measured using Return on Assets (ROA). A quantitative approach with multiple linear regression analysis was employed, and the sample was selected using purposive sampling. The results reveal that capital structure has a significant positive effect on ROA, whereas CSR disclosure has a significant negative effect on ROA. These findings suggest that strategic use of long-term debt can enhance profitability, while the costs and commitments arising from CSR disclosure may reduce financial performance. The study implies that company management should optimize capital structure and carefully balance sustainability strategies through CSR disclosure to avoid diminishing profitability.

This study compares the financial performance and corporate social responsibility disclosure in Indonesian telecommunications companies listed on the Indonesia Stock Exchange in 2018-2021 before and during the Covid-19 pandemic. This study used purposive sampling; namely, 18 companies were found to be eligible as samples. The data used in this study is secondary data taken from the official website of the Indonesia Stock Exchange. The analysis technique used is descriptive statistics, normality test, and different test (paired sample t-test). The study results show differences in the return on equity, current ratio, and debt to equity ratio before the co-19 pandemic and during the co-19 pandemic. Meanwhile, corporate social responsibility did not differ before the COVID-19 pandemic and during the COVID-19 pandemic.

The research aims to analyze the determinants of cybersecurity risk disclosure (CSRD) in Saudi Arabia and discover the influence of CSRD on both firm value and stock return volatility. The study used a mixed-methods approach that combines qualitative and quantitative techniques to determine the relationships used by the content analysis method to analyze the annual financial reports of Saudi firms for the period from 2015 to 2022, to estimate the volume of CSRD, firm value, and stock return volatility. The results of the study show that the impact of a firm’s size, age, leverage, and profitability are positive and significant on CSRD. In contrast, free cash flow has no significant effect on CSRD. Moreover, a curvilinear relationship exists between operating expenses and CSRD. In addition, Firm value is positively and significantly correlated with CSRD and many firm characteristics. However, stock return volatility is negatively and significantly correlated with CSRD in the Saudi business environment.

Purpose – We examine the effects of environmental, social, and governance (ESG) disclosure and green building policies on the stock returns of Islamic and conventional banks. Methodology – Data were obtained from 17 Islamic banks and 17 conventional banks from eight countries (Arab Saudi Arabia, UAE, Qatar, Kuwait, Malaysia, Indonesia, Pakistan, and Bahrain) over seven years from 2017 to 2023. We conducted panel least squares with fixed effects (dummy variables) for cross-sections using EViews to process the data.Findings – The estimated results show that the green building policy variable is statistically significant to the stock return of Islamic banks, while the environmental, social, and governance variables are not. Meanwhile, the social dimension is statistically significant for the stock returns of conventional banks, but environmental, governance, and green buildings are not. Implications – Investors and policymakers should consider the implementation of ESG and green building policies to contribute on sustainability issues and gain financial return.Originality – This study tests non-financial performance, such as ESG disclosure and green building policy, on the stock returns of Islamic and conventional banks, which has not been extensively studied by the existing literature.

Making investment decisions in the capital market requires financial and non-financial performance information. One of the non-financial information is information regarding sustainability. This information can be seen in the Corporate Social Responsibility Disclosure (CSRD) and Environmental Performance (PROPER). This research analyzes the effect of CSRD on Stock Return with PROPER moderation. This research is descriptive quantitative research using secondary data and taking samples using a purposive method from in consumer cyclicals and consumer non-cyclicals sub-sector manufacturing companies listed on the Indonesian Stock Exchange from 2017 to 2021. The number of samples that meet the requirements is 11 companies. The research uses multiple regression analysis. The results of this research show that CSRD has no influence on Stock Return, and PROPER has not been able to significantly strengthen the influence of CSRD on stock return. This research concludes that CSRD information directly or moderated by PROPER does not provide a positive signal for investors. The implication of this research is that PROPER information in CSRD can provide a strengthening signal to investors which influences share prices.

This study investigates the impact of narrative R&D disclosure characteristics—readability, sentiment, and quantity—on stock return volatility. A comprehensive longitudinal regression model that includes these three characteristics, along with their interactions with R&D investment intensity, outperforms the other models. The readability of narrative R&D disclosures significantly affects volatility, with more readable disclosures associated with reduced fluctuations. Additionally, readability moderates the effect of R&D investment intensity on volatility. No direct effect of disclosure sentiment on stock return volatility is observed, but disclosure sentiment influences the relation between R&D investment intensity and stock return volatility. Finally, there is a strong positive correlation between the quantity of R&D disclosures and stock return volatility, suggesting that excessive R&D information tends to increase stock return fluctuations. The study provides practical implications for both investors and firms. It suggests that investors may consider R&D disclosure characteristics to better assess the risk of stock return fluctuations when selecting shares of R&D‐intensive firms and offers guidance for firms on delivering clearer and more balanced R&D disclosures to help reduce market volatility.

This research aims to assess how financial leverage affects stock returns while taking into account CSR disclosure as moderation. The research data were analyzed using panel data analysis. Secondary data in the study were taken from the 2013-2022 annual reports of banks listed on the Indonesia Stock Exchange (BEI). To measure the level of corporate social responsibility disclosure, the reporting standards of the Global Reporting Initiative (GRI) available through the ESGI data site were used. Sample selection was conducted using purposive sampling technique, which is a method that selects samples based on certain criteria relevant to the research objectives, and only samples that meet these criteria are used in the analysis. Data analysis using eviews12 which includes descriptive statistics, chow test analysis, classical assumption test, and equation (hypothesis testing). The research findings show that, when measured by the DER indicator, financial leverage significantly effects stock returns. However, if the DAR indicator is used to measure financial leverage, stock returns will decrease or have a negative effect. CSR disclosure moderateskthe effect ffinancial leverage on stock returns, if financial leverage uses the DER indicator in its measurement. However, CSR disclosure is not able to moderate the effect of financial leverage on stock returns if financial leverage uses DAR in its measurement. This study has several limitations, including a limited focus on the banking sector. In addition, the measurement of CSR disclosure is not based on specific criteria, but is adjusted to the disclosure of each company.

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Purpose: This study aims to examine the effect of environmental, social, and governance (ESG) disclosure on stock returns with the moderating effect of tax rate, family ownership and foreign board members in Egypt between 2020 and 2024.Methods: Using a panel of 735 firm-year observations for the top 100 Egyp firms listed on the EGX, the study employs ordinary least squares (OLS) regression together with industry- and year-fixed effects. The analysis controls for size, profitability, leverage, and capital intensity at the firm level.Results: The results indicate that ESG disclosure is positively and significantly related to return (β2 = 0.169), which means that stronger ESG disclosure is connected with better stock performance. Furthermore, tax rate (ETR) has a negative influence on ESG disclosure, and family and foreign board members positively influence the ESG disclosure.Novelty: This is the first study that provides insights into the determinants of ESG disclosure in an emerging market (Egypt) and the economic implications.Implications: These findings underscore the need for ESG transparency for investors and policymakers, and the role of governance in enabling sustainable financial performance

This study aims to examine the effect of Environmental, Social, and Governance (ESG) disclosure and energy price volatility on stock returns of energy sector companies listed on the Indonesia Stock Exchange (IDX) during the 2022–2024 period. A quantitative approach was employed using multiple linear regression as the analytical method. The sample consisted of 10 energy companies selected through purposive sampling, based on the availability of sustainability reports, stock price data, and research completeness. The results indicate that ESG disclosure has a positive and significant effect on stock returns, suggesting that companies with higher sustainability transparency tend to gain stronger investor confidence. Energy price volatility also shows a positive and significant effect on stock returns, reflecting the sector’s sensitivity to global energy price dynamics. Simultaneously, both variables significantly influence stock returns, although the relatively low coefficient of determination implies that other factors should also be considered. This study highlights the importance of integrating internal factors (ESG) and external factors (energy price volatility) for investors when making investment decisions in the energy sector.

Purpose –Company performance can be measured in terms of financial and non-financial. Shareholders are motivated to invest their capital in the hope of getting a return in accordance with the capital invested. Indonesia is currently in a dilemma with a knowledge-based, fast-changing and technology-based economy. Most companies use technology to improve the efficiency of company activities and reduce costs incurred. Issues related to the environment and the adoption of green technology have received much attention over the past few years. The purpose of this study is to determine the relationship between Green technology, environmental disclosure and green intellectual capital on stock returns Design/methodology/approach –The type of data in this study is secondary data. The samples used in the study were Basic Materials and Transportation & Logistic companies with a sample size of 267. Findings –The results show no effect of green technology on stock returns. There is a significant negative relationship between Green Intellectual Capital and stock returns. While environmental disclosure has a significant positive effect on stock returns. Research limitations/implications –In developing countries, no statistically relevant relationship was found between fair valuation and earnings quality, which may be due to the adoption of IFRS and the lack of experience in fair valuation, or more generally, the very low influence of IFRS regulations on local accounting practices. Originality/value –In this paper, researchers use clear technology to measure green technology that has not been used by previous researchers.

Purpose – This study aims to analyze the effect of Sustainability Report Disclosure, Firm Size, and Green Accounting on Return on Assets (ROA) of basic materials sector companies listed on the Indonesia Stock Exchange for the period 2020-2024. Design/methodology/approach – This research employs a quantitative approach with causal-explanatory design using secondary data obtained from annual reports and sustainability reports. The sample consists of 16 basic materials sector companies selected using purposive sampling technique, resulting in 80 observations during 2020-2024. Data analysis was conducted using panel data regression with Random Effect Model (REM) approach, supported by EViews9 software. Variable measurement uses disclosure index based on GRI Standards 2021 for Sustainability Report, natural logarithm of total assets for Firm Size, and dummy variable for Green Accounting based on environmental cost disclosure. Findings – The results showed that the overall model was significant (F-statistic = 2.245; p-value = 0.090), explaining 8.14% of the variation in ROA (R² = 0.814). Individually, the Sustainability Report Disclosure variable had no effect on ROA (coefficient = 0.0034; p-value = 0.4998), indicating that corporate sustainability transparency has not been able to improve asset profitability. Firm size did not affect ROA (coefficient = 0.0008; p-value = 0.6878). The results showed that firm size does not directly reflect a company's ability to generate profits from its assets. On the other hand, Green Accounting shows a negative effect on ROA (coefficient = -0.0608; p-value = 0.0163), this can be interpreted that the costs arising from the implementation of Green Accounting in the short term have the potential to reduce the company's profitability, although in the long term it can provide non-financial benefits such as reputation and business sustainability. Practically, companies that implement Green Accounting (disclose environmental costs in sustainability reports) have a lower ROA of 6.1 points compared to companies that do not implement it, assuming other variables are constant. Research limitations/implications – This research was obtained from financial reports and sustainability reports. The data obtained only covers a five-year period and may not fully capture the quality or substance of the disclosures, but only the quantity. JEL: G3, Q5

Negative sentiments stemming from declining aluminum exports, coal price fluctuations, a global economic slowdown, and environmental pressures influence investors' perceptions of investing in the raw materials sector. CSR disclosure and implementing Good Corporate Governance principles through audit committees, independent commissioners, and managerial ownership are vital strategies for enhancing investor confidence and long-term corporate value. This study investigates the effect of CSR disclosure and corporate governance attributes on corporate value, with Return on Assets as the control variable. It utilizes secondary data from annual and sustainability reports, focusing on companies in the basic materials sector listed on the Indonesia Stock Exchange from 2020 to 2023. Quantitative methods are employed, with panel data regression analysis. The results indicate that, partially, CSR disclosure has a negative and significant impact on corporate value, whereas the proportion of the audit committee exhibits a positive and significant effect. The frequency of audit committee meetings and the proportion of independent commissioners do not demonstrate a significant impact. Audit committee competence positively and significantly influences corporate value, while managerial ownership adversely and significantly affects it. Collectively, these variables influence corporate value. These findings provide valuable insights for company management to enhance CSR and governance practices, thereby increasing the company's appeal to investors

This study assesses the effect of environmental information disclosure on stock price reactions among the listed consumer goods firms in Nigeria. Data for the study was collected from 16 consumer-goods firms listed in the Nigerian Stock Exchange (NSE) from 2014 to 2022. The statistical analysis technique utilized for the study is the panel data regression. The study used secondary data from 28 consumer goods firms listed on the Nigeria stock exchange. Empirical results show that environmental information disclosure positively affects stock price reactions after controlling for firm size and leverage. It was also found that the disclosure of environmental information significantly impacts the return on assets, which is the proxy for the firm’s profitability. Based on these findings, the study concludes that environmental information is an essential tool for improving the information content of a firm’s value and profitability. It is therefore recommended that a robust legal and regulatory framework for environmental information disclosure be established in the country and that firms should strive to provide more awareness about environmental information disclosure to their stakeholders.

Today is the era when sustainability has become an important part of business strategy, and companies are expected to demonstrate environmental responsibility without sacrificing financial performance. This study examines the impact of carbon emission disclosure and carbon tax on financial performance using data from companies listed in the 2023 SRI-KEHATI Index, which is a green stock index, which generally already has an emission and energy efficiency strategy. The measurement of financial performance is based on Return on Assets (ROA), while carbon emission disclosure is assessed based on the GRI 305 disclosure score. A quantitative approach was applied by analyzing cross-sectional data from 24 Indonesian companies that have committed to social and environmental responsibility. To perform the analysis, multiple linear regression was applied in this study. It was found that carbon emission disclosure positively and significantly affected financial performance, indicating that transparent environmental reporting can increase public and stakeholder trust, as well as provide financial benefits. In contrast, the carbon tax variable did not have a significant effect on ROA. These findings emphasize the importance of integrating sustainability strategies into corporate management, especially in emerging markets.

This study aims to analyze the effect of financial performance and firm value on Environmental, Social, and Governance (ESG) disclosure with Accounting Information Systems (AIS) as a moderating variable in manufacturing companies listed on the Indonesia Stock Exchange for the 2022-2024 period. This study uses a quantitative approach with secondary data obtained from annual reports and corporate sustainability reports. The research sample was determined using a purposive sampling technique to obtain companies that meet the research criteria. Financial performance variables are proxied by Return on Assets (ROA), Return on Equity (ROE), and Net Profit Margin (NPM), firm value is measured using Price to Book Value (PBV), ESG disclosure and AIS are measured using dummy variables. Data analysis was performed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method with the assistance of SmartPLS software. The results show that financial performance has a positive and significant effect on ESG disclosure. Firm value also has a positive and significant effect on ESG disclosure. In addition, financial performance has a positive effect on firm value. Accounting Information Systems have been shown to moderate the influence of financial performance on ESG disclosure, but not the influence of firm value on ESG disclosure. This research is expected to provide empirical contributions to the development of sustainability accounting literature and provide considerations for management and investors in decision-making.

Purpose: Analyze the impact of mandatory disclosure of ESG information in Brazil on the price of shares in the local market. Methodology: This analysis is carried out through an event study, which refers to the publication of the Reference Form (FRE) of companies in the metal and mining sector - that are environmentally sensitive. The date of the event is the date of disclosure of each company's FRE, for the fiscal year ending on December 31, 2022. Results: The results confirm the main hypothesis of this study: The mandatory disclosure of sustainability practices by Brazilian companies impacts the return on their shares. It is possible that the market understands that the short-term costs - related to the implementation of sustainability practices - still exceed the benefits that will lead to the appreciation of companies in the long term. However, the progressive integration of global markets foresees an alignment of sustainable practices that should reach the Brazilian market in the medium term. Contributions of the Study: This research differs from others by finding that the mandatory disclosure of sustainability practices - by Brazilian companies - negatively impacts the return on their shares, after the implementation of Resolution no. 59/2021. It contributes to academia with the empirical analysis of the market efficiency hypothesis. Furthermore, its results can help companies, investors and the capital market to better understand the initial effects of mandatory publication of sustainable practices. Finally, this study also contributes to the stimulation or adaptation of policies defined by regulatory bodies on sustainability issues.

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ABSTRACT A growing number of firms are openly disclosing knowledge through academic journals and conferences; however, the impact of this practice on their market value needs further research. From a signalling theory perspective, we investigate the relationship between open knowledge disclosure and firm value and identify potential contingency factors. We propose that open knowledge disclosure conveys a firm’s technical capability and commitment to open science, consequently contributing to its market value. Drawing upon data from listed companies within China’s information and communication technology sector, we confirm that open knowledge disclosure enhances firm value. Furthermore, this enhancement is more pronounced for small firms, young firms, private firms, firms with few patents, firms drafting few technical standards, or firms operating in an immature technology market. Our findings suggest that firms, especially those facing high information asymmetry or lacking alternative signals, can increase their market value by sending positive signals through open knowledge disclosure.

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The unprecedented proliferation of standard-essential patents (SEPs) has exerted a transformative impact on the evolution of standards, fueling global technological innovation. Recognized as a linchpin for enabling rapid and widespread innovation diffusion, SEPs have become pivotal drivers of contemporary business strategies. While the superior value of SEPs vis-à-vis non-standard-essential patents (non-SEPs) is widely acknowledged, the intricate process underlying the transformation of patents into SEPs warrants a comprehensive investigation. Drawing on an extensive analysis of 4859 SEPs and 775 669 non-SEPs granted by the United States Patent and Trademark Office from 1976 to 2014, this study employs a rigorous examination of ten commonly utilized patent indicators, delineating four critical dimensions: 1) collaboration, 2) knowledge, 3) diversity, and 4) legal protection. Through a strategic lens, this study shed light on the multifaceted factors that amplify the likelihood and hasten the speed of SEP declarations. The empirical findings of this study compellingly illustrate the predominantly positive influence exerted by most indicators on SEP declaration likelihood, effectively expediting the declaration process.

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This article presents evidence that patent value varies with random examiner assignment at the U.S. Patent Office. Prior work analyzed firm growth as a function of review by “easy” examiners who grant patents at a high rate. The current research looks past whether a patent is granted and instead focuses on how assignment to an “easy” or “hard” examiner influences the attributes of resultant patents. Focusing on their propensities to reject applications on novelty or obviousness grounds, analysis finds that patents issued by lenient examiners tend to be broader in scope, are more valuable to their owners, and elicit a larger stock market response when granted. Further analysis quantifies the level of variation (“noise”) among examiners. This inquiry finds that the noise level in issuing novelty rejections decreases with examiner experience, while variation among examiners issuing obviousness rejections actually increases with experience. A third line of investigation presents evidence that “stricter” examiners disproportionately reach the correct examination relative to more lenient counterparts. This conclusion is supported by “twin application” analysis comparing outcomes of related U.S. and European applications. Consistent with the literature using this method, the European Patent Office's outcome is considered the “gold standard” for examination, and thus, its decision to grant or deny is assumed correct.

Purpose This study aims to explore how environmental investments impact the firm financial outcomes in emerging markets using a sample of 4,081 firms across 25 emerging countries from different regions from 2010–2022. Design/methodology/approach Fixed effect regressions with robust standard errors for unbalanced panel data are used to investigate the impact of Environmental, Social and Governance (ESG) disclosure scores and carbon emissions intensity on firm profitability. The authors used simultaneous quantile regressions with bootstrapped standard errors to allow for estimating parameters of different quantiles of superior and inferior financial performers. Non-linear regressions are used to test for curvilinear relationships. Two-stage least squares regressions are used to mitigate concerns of endogeneity. Findings The results reveal that firms with less emissions of carbon dioxide report high profitability, however, firms with high ESG disclosure scores do not achieve superior performance. The authors detect a positive curvilinear U-shaped relationship and determine threshold level of ESG scores. Furthermore, firms with sustainable investments have more resilient performance during COVID-19 pandemic. Research limitations/implications A comprehensive analysis of the complex effect of environmental sustainability on financial performance in emerging markets uncovers the strategic motivations behind ESG disclosures and the thresholds where environmental performance translates into financial gains. Overall, this study emphasises the significance of sustainable investments in enhancing long-term profitability and resilience in emerging markets during turbulent times. Practical implications Proactive carbon emission reduction strategies are essential to safeguard firm competitive advantage. Firm ESG investments should be considered when forecasting firm value and stock price. There is a growing need for rigid policies to promote a green economy and mitigate climate change risks. Originality/value Offers a unique setting to examine the association between firm environmental and financial performance across emerging countries and regions. It explores the non-linear shape and magnitude of this relation across high-low quantiles of profitability. It sheds new light on the impact of sustainable practices on firm resilience during COVID-19 pandemic.

This study aims to examine the relationship between economic policy uncertainty (EPU) and earnings management practices. Specifically, this study explores how a firm’s perception of economic policy uncertainty (FPU) shapes reporting decisions, including earnings management. Based on a sample of US firms over the period 2001–2020, the authors use a new proxy of EPU at the firm level based on textual analysis of conference call transcripts. The results show that firm’s perception of economic policy uncertainty (FPU) is negatively associated with accruals earnings management (AEM), suggesting that under conditions of heightened uncertainty, managers have incentive to improve transparency and reduce discretionary accruals to reassure investors and reduce perceived risk. Moreover, this study finds that firms with high agency costs are even more likely to reduce AEM during periods of elevated policy uncertainty. Under such conditions, managerial discretion is constrained, possibly due to increased monitoring or reputational concerns. Robustness tests also reveal that firms may engage in both accrual-based and real earnings management (REM) simultaneously, and that the observed accrual effects are not mechanically driven by structural operational changes, but rather reflect managerial reporting behavior. Cross-sectional analyses further show that firms with greater external capital dependence, rapid sales growth and higher exposure to uncertainty are more likely to engage in AEM, highlighting the role of firm-specific incentives. Interestingly, the results suggest that local political orientation (e.g. Democratic, Republican or swing states) does not significantly influence the effect of FPU on earnings management. Perceived uncertainty within the firm is more important than external political alignment. This study has direct implications for market participants, firms and regulators. Understanding the nuanced relationship between firm policy uncertainty and earnings management can help investors and analysts recognize the propensity for earnings management in uncertain economic environments, especially among firms with significant agency costs and external capital requirements. This study also underscores the importance of robust corporate governance and monitoring mechanisms, particularly in firms with high free cash flow and uncertainty. Implementing stringent oversight can mitigate the propensity for managers to engage in REM, thereby protecting shareholder interests and preserving firm value. Finally, regulators and auditors need to be vigilant about earnings management. Enhanced due diligence, robust auditing standards and transparent disclosure practices are essential to ensure the integrity of financial reporting during periods of uncertainty. This study focuses on the mechanism of firm-specific perception of policy uncertainty. This firm-level perspective provides a better understanding of how EPU influences corporate earnings management and allows to capture the heterogeneity in perceived uncertainty at the firm level.

: This study is one of the first empirical evaluations of voluntary IFRS adoption in Japan, and it shows that the earnings announcements of Nikkei 225 firms using IFRS have higher value relevance than earnings announcements of Nikkei 225 firms using Japanese GAAP (earning announcements from 2008-2022). This study uses value relevance methodology that relates surprise earnings (calculated with Bloomberg-compiled analyst earnings expectations) to abnormal returns over the 12 months before the announcement, a methodology evolved from the seminal work of Ball and Brown (1968). Another finding here is the strength of qualitative variables to measure surprise earnings. Japan represents a unique opportunity to compare IFRS to a local standard in a large, developed economy using similar companies except for the accounting standard. These results provide essential data to the IFRS literature, stakeholders navigating the Japanese accounting environment, and other jurisdictions weighing the benefits of IFRS.

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This article aims to offer a novel answer to the following question: How can firms use artificial intelligence (AI) technology to create and capture value, specifically through predictive machine learning? This article analyses ten papers by the same author on the themes of value creation through AI. These papers include conceptual research, empirical cases, and case-based theory building. These exploratory cases explore the management of AI capabilities in business models using a variety of methodologies, including systematic reviews, statistical regression, and qualitative comparative analysis (QCA). To enhance the theoretical and practical insights arising from this research, the article adds a regulatory dimension to the analysis by discussing the European Union (EU) Artificial Intelligence Act. The results show that AI can create perceived user value and enable the realization of data network effects. When applied within a firm’s business model architecture, AI can activate one or more of the four available business model themes (novelty, efficiency, complementarity, and lock-in) that account for value creation and capture. This study contributes to understanding how a firm can use this new technology to create value. The findings suggest that integrating AI into business models is essential for delivering user value and fostering data network effects. Managers play a crucial role in coordinating AI deployment across all business activities. The findings reveal that firms must not only activate the appropriate business model themes (e.g., novelty, efficiency, and lock-in) but also ensure compliance with evolving regulatory standards to secure sustainable competitive advantage. This study adopts a multitheoretical approach based on business model theory and the theory of data network effects. However, authors of further studies should consider using large samples and testing the findings in different contexts to enhance generalizability.

This study explored how investment in Information and Communication Technology (ICT) moderated the relationship between ownership structure and firm value among listed financial firms in Nigeria. Drawing from the Resource-Based View (RBV) and Stakeholder Theory, it examined whether ICT enhanced the strategic roles of different ownership types individual, block institutional, managerial, and foreign ownership. Data from 41 listed financial firms covering the period 2013–2023 were analyzed using panel regression to assess both direct and interaction effects. Findings revealed that individual ownership did not significantly affect firm value on its own, but became significant when moderated by ICT, suggesting that digital tools improved individual investors' access to firm disclosures. Similarly, Institutional ownership had no significant standalone effect but became significant with ICT, indicating enhanced monitoring capabilities through digital platforms. Managerial ownership had a positive but statistically insignificant effect, both directly and when moderated by ICT, implying that existing ICT tools did not sufficiently support managerial accountability or align management interests with firm value. Foreign ownership had a significant positive effect on firm value, but ICT did not significantly moderate this relationship likely due to strong governance practices already embedded in foreign investment frameworks. Importantly, ICT on its own had a strong and positive effect on firm value, underscoring its strategic importance in Nigeria's financial sector. Based on these insights, the study recommended that financial firms adopt mobile-friendly digital platforms to support individual investor engagement. For institutional investors, AI-driven governance and real-time compliance tools should be implemented. ICT systems should also be integrated into executive performance tracking to strengthen managerial accountability. For foreign investors, platforms should be aligned with international standards and offer multilingual support. Overall, sustained investment in robust ICT infrastructure, data analytics, and cybersecurity remained essential to enhancing firm value across Nigeria's financial industry. Keywords: Individual Shareholding; Institutional Ownership; Managerial Ownership; Foreign Ownership; Firm Value; ICT Investment

PurposeThis research explores how firms manage the complex technologies standardization in action groups. It considers the strategic issues that technology producers face when involving lead users in architecture design. Drawing on the multi-mode standardization literature, this study addresses two dilemmas regarding value creation and appropriation by technology producers within coalitions. The first dilemma is how to create value by developing solutions in compliance with industry standards. The second one is how to appropriate value while ensuring the technology sharing with action groups. The answers to these two dilemmas contribute to filling the research gap on value creation and appropriation in multi-mode standardization.Design/methodology/approachThe research focuses on technology producers participating in action groups where lead users play a crucial role. We conducted a qualitative analysis based on the standardization experience of a Japanese company specializing in smart robotics. Data are collected through semi-structured interviews with key actors. Action groups are defined operationally as a set of stakeholders including competitors of the technology producers, component suppliers, end users, services providers, research centers and academia. The case study is suitable for highlighting specific aspects of the standardization process during its manifestation. It reveals how firms create and appropriate value, providing details about its standardization strategy.FindingsOur findings show that smart robotics standardization is drivn by collaborative models, where the two dilemmas of value creation and appropriation are evident. Firstly, the case revealed that standardization is lead users oriented. Secondly, lead users’ involvement is crucial to customize technologies. Thirdly, the firm’s position is to share a part of the value with the members. The IPR policy is a matter of interest within action groups, since the collaboration is based on open innovation models to share patents and licenses related knowledge.Research limitations/implicationsThis research has some limitations attributable to the limited generalizability of the results due to the qualitative analysis. In addition, this study considers the perspective of technology producers, but should also take into account the perspective of both collective actions itself and the lead users. Findings have some implications in the strategy negotiation. Participating in action groups is not enough to ensure a competitive advantage. Involving lead users is of strategic importance to acquire a competitive advantage. Lead users contribute to the producers’ technology design, helping firms to differentiate solutions from the industry standard and create value from customized technologies.Practical implicationsThis study helps practitioners understand the competitive side of collective actions, clarifying the value capture and appropriability in standardization. The research provides insights to policymakers and standard development organizations committees when they are called to harmonize standards considering the fallouts on the sector’s competitiveness. Findings suggest appropriate property rights policies to manage the issues related to the value appropriability and technology sharing, recognizing action groups members for their contribution in value creation.Originality/valueThis study shows how firms deal within action groups with the two dilemmas of variety versus technology conformity and property rights versus technology sharing. It fills the research gap in collective actions, emphasizing the perspective of the individual firm in the group rather than the coalition strategy itself. This topic highlights the crucial role of lead users within action groups in managing the two dilemmas, offering a new perspective for understanding critical issues of multi-mode standardization. Reflecting on mechanisms and tools to manage the two dilemmas allows firms to protect their competitive advantage in coalitions.

Research aims: ISSB issued IFRS S1 and S2 reporting that influence entities to disclose information about risks and opportunities based on SASB standards. This study examine whether materiality disclosure reflects on value relevance information content. The materiality item relates to the general purpose of financial reporting, helping users make decisions.Design/Methodology/Approach: Using regression analysis, this study analyse 330 firm-year observations from 71 firms listed on the Indonesia Stock Exchange from 2017-2022.Research findings: This study find positive relationship between materiality disclosure and value relevance information. In addition, firms with high materiality not accurately reflects on stock price related sustainability item.Theoretical contribution/Originality: This study provides novel evidence that materiality disclosure enhances value relevance by influencing stock prices. It extends the decision-usefulness perspective by showing that market responses to materiality vary across firms, highlighting the role of sustainability disclosure in shaping investors’ valuation.Practitioner/Policy implication: This study suggest that materiality disclosure serves as a strategic tool for firms to enhance market value, not just a compliance exercise. For regulators, the evidence supports ISSB’s mandate that materiality reporting is essential to provide decision-useful sustainability information for investors.Research limitation/Implication: First, this study is limited to the Indonesian context. Future research is encouraged to broaden the scope by including other countries, particularly those that are members of the IASB. Second, the study does not account for the issue of endogeneity within its methodological approach.

ABSTRACT Research has highlighted the role of patent portfolio strategy on firms’ innovation without elaborating on how to layout the diversity of patent portfolios to improve innovation quality. Drawing on the resource-based view and institutional theory, this research attempts to extend patent portfolio–innovation debate by examining the moderating effects of policy or specifically, technology standard on the patenting–innovation relationship. We argue that the leverage of technology standard could help firms optimise patent portfolios by accessing information, reducing transaction costs, and promoting complementary innovation, and therefore leads to higher innovation quality. Moreover, considering heterogeneous technology standards, in the context of relatively complex and rapidly updated technology standards, specialised patent portfolios are more significant for firms to enhance substantive innovation. Our analysis of data from 729 Chinese manufacturing listed companies provides supports for both the moderating role of technology standard on the patent portfolio–innovation relationship. This paper contributes to patent management literature by elaborating upon the mechanisms of how technology standard can be leveraged to capture value from firms’ patent portfolios.

We examine firms’ voluntary disclosures of innovation under technology coopetition, focusing on technology standard setting organizations (SSOs). Technology coopetition is characterized by (1) cooperation to determine technology standards, which requires information sharing to reach consensus, and (2) competition for standard implementation to obtain standard-essential patents, which create incentives for firms to deviate from the expected level of information sharing. We document a decrease in 10-K narrative R&D disclosures, more generic 10-K narrative R&D disclosures, and a longer delay of patent disclosures via the USPTO after a firm joins an SSO. Among alternative explanations, our evidence is most supportive of the hypothesis that firms strategically withhold innovation information. JEL Classifications: L15; M41; O32.

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The impact of mergers and acquisitions on corporate research and development has attracted considerable attention. We examine whether acquisitions of firms with AI patents mitigate post-merger declines in patent quality using computational text analytics and event-time benchmarking. We find that the 90th percentile of forward citations does not fall, that the average abstract length, our disclosure proxy, rises slightly, and that changes in lexical diversity are small, favoring mitigation over degradation. These findings suggest that artificial intelligence is likely to have a positive impact on the efficiency of research and development in the context of mergers and acquisitions.

This paper provides novel empirical evidence that patents enable knowledge disclosure. The analysis exploits the Invention Secrecy Act, which grants the U.S. Commissioner for Patents the right to prevent disclosure of new inventions that represent a threat to national security. Using a two-level matching approach, we document a negative and large relationship between the enforcement of a secrecy order and follow-on inventions, as captured with patent citations and text-based measures of invention similarity. The effect of secrecy orders is particularly salient for geographically-distant parties and for inventions in the same technological field as the secreted patent.

PurposeThe paper examines whether, along with the financial performance, the disclosure of research and development (R&D) expenses, patent portfolios, patent citations and innovation activities affect the market capitalization of Russian companies.Design/methodology/approachThe paper opted for a set of techniques including bag-of-words (BoW) to retrieve additional innovation-related data from companies' annual reports, self-organizing maps (SOM) to perform visual exploratory analysis and panel data regression (PDR) to conduct confirmatory analysis using data on 74 Russian publicly traded companies for the period 2013–2019.FindingsThe paper observes that the disclosure of nonfinancial data on R&D, patents and primarily product and marketing innovations positively affects the market capitalization of the largest Russian companies, which are mainly focused on energy, raw materials and utilities and are operating on international markets. The study suggests that these companies are financially well-resourced to innovate at risk and thus to provide positive signals to stakeholders and external agents.Research limitations/implicationsOur findings are important to management, investors, financial analysts, regulators and various agencies providing guidance on corporate governance and sustainability reporting. However, the authors acknowledge that the research results may lack generalizability due to the sample covering a single national context. Researchers are encouraged to test the proposed approach further on other countries' data by using the compiled lexicons.Originality/valueThe study aims to expand the domains of signaling theory and market valuation by providing new insights into the impact that companies' reporting on R&D, patents and innovation activities has on market capitalization. New nonfinancial factors that previous research does not investigate – innovation disclosure indicators (IDI) – are tested.

This study mainly aims to analyse whether innovation related to the circular economy's principles, defined as ‘circular innovation’, is closely linked to environmental disclosure and contributes to the generation of improvements in environmental performance. The article also aims to test whether waste patents, as a specific indicator of circular innovation, are more valuable assets that need specific strategic management due to their level of novelty, complexity, and radicalness being higher than other green or conventional innovations. The empirical analysis uses a rich firm‐level dataset of worldwide companies for the 2011–2015 period, with 1330 observations to show that environmental disclosure and environmental performance are positively associated with circular innovation. This innovation is in fact, more intensive in patent claims and patent citation generation, indicating a higher economic value for companies. The article contributes to this new line of inquiry on defining circular innovation, as well as providing some environmental determinants and consequences from the stakeholders' perspective in relation to circular business models.

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Conceptualizing standard-setting organizations SSOs as technological arenas within which firms from different countries interact and learn, we offer insights into the interplay between firms' institutional logics and their interorganizational learning outcomes. We suggest that firms' interorganizational learning is embedded in their macrolevel country contexts, characterized by more corporatist versus less corporatist pluralist institutional logics. Whereas corporatism spurs coordinated approaches, pluralism engenders competitive interactions that affect the extent to which firms span organizational and technological boundaries and learn from each other. We test our theory using longitudinal analysis of 181 dyads involving 26 firms participating in 17 SSOs in the global mobile handset industry. We find that interorganizational learning, as measured by patent citations, involving corporatist firm dyads significantly increases when the dominant logic within the arena is also corporatist. By making cooperative schemas more accessible, a dominant corporatist logic also enhances interorganizational learning across technologically distant dyads. When a pluralist logic dominates the arena, corporatist dyads learn less because firms in the dyad activate a contradictory logic that decouples them from their natural processes for interorganizational learning. These findings highlight the implications of institutional logics for interorganizational learning outcomes and provide insights into how firms attend to institutional contradictions in arenas that provide opportunities for interorganizational learning.

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Patent holders are, with increasing frequency, making public promises to refrain from asserting patents under certain conditions, or to license patents on terms that are “fair, reasonable and non-discriminatory” (FRAND). These promises or “patent pledges” generally precede formal license agreements and other contracts, but are nevertheless intended to induce the market to make expenditures and adopt common technology platforms without the fear of patent infringement. But despite their increasing prevalence, current contract, property and antitrust law theories used to explain and enforce patent pledges have fallen short. Thus, a new theory is needed to secure the market-wide benefits that patent pledges can offer. This article proposes a novel “market reliance” theory for the enforcement of patent pledges. Market reliance is rooted in the equitable doctrine of promissory estoppel, but adds a rebuttable presumption of reliance borrowed from the “fraud-on-the-market” theory under Federal securities law. Under this approach, a patent holder’s public commitment is enforceable by any participant in the relevant market, absent a showing that it knowingly rejected the commitment. The market reliance theory offers a robust means for enforcing legitimate patent pledges by third party market participants, and extends the effect of such pledges to downstream purchasers of patents. As such, the market reliance theory could fill a critical gap in the existing patent enforcement landscape and give greater assurance to the technology markets that depend on them.

B a c k g r o u n d . Legislative and regulatory changes are reshaping the landscape of Environmental, Social, and Governance (ESG) principles. This is prompting companies to reassess their commitments and implement new business development strategies that align with sustainable development. At the same time, certain "anti-ESG" trends, primarily in the US, are creating uncertainty regarding ESG performance dynamics, and the risk-return correspondence dynamics also appear unstable. These developments motivated the study's purpose: to examine the dynamics of ESG and risk-return correspondence from 2019 to 2024. This paper presents results on the dynamics of 10 ESG subindexes and the K-ratio indicator for the largest US companies from 2019 to 2024. We also analyzed the hypothesis concerning the correspondence between ESG performance and the investment risk-return ratio. M e t h o d s . Employed methods include factual and comparative analysis, statistical techniques, correlation estimation, risk-return measurement, synthesis, and deduction. R e s u l t s . We estimated the dynamics of subindices for 30 US companies from the Dow Jones Industrial Average (DJIA) index basket using statistical analysis of ESG scores. The subindexes exhibited convex upward trajectories, reaching their highest scores in the year when the COVID-19 pandemic began. Analyzing risk-return correspondence from 2019 to 2024 revealed persistent and significant changes in this ratio's value. A statistical analysis of the relationship between ESG scores and risk-return correspondence showed that there is no clear correlation between these two components. Therefore, it can be concluded that these two factors have distinct dynamics and that there is no clear relationship between ESG performance and market risk-return outcomes. C o n c l u s i o n s . ESG principles are becoming an important component of sustainable business development; however, this process remains multifaceted and complex for large companies. The implementation of ESG scoring systems by several global rating agencies enables the identification and analysis of numerical patterns of this development. Current research focuses on analyzing the impact of ESG reporting on corporate financial and investment performance. Identifying such regularities will improve our understanding of the role and significance of ESG in the holistic analysis of corporate development.

This study investigates the determinants influencing retail investors' capital allocation to sustainable financial products, focusing specifically on Germany—a pertinent case due to its strong commitment to sustainability, supportive regulatory environment and substantial market growth. We utilised two surveys conducted in 2020 by the 2° Investing Initiative and Choyze GmbH in collaboration with the German Environment Agency to obtain a measurement invariant combined dataset. Utilising robust and generalised linear modelling and interpretable machine learning techniques, our analysis identified that three primary motivation components—personal values, social and environmental impact and investment return—exhibit significant overlap. The findings demonstrate that investment motivations are consistently predicted by sustainability interests. In contrast, socio‐demographic factors (age, gender, education, household income) exhibit inconsistent patterns across investment motivations and exert weaker influence. As a result, we propose robust models to predict sustainable investment motivation and highlight their consistency and applicability in the green finance sector.

The turnover intention of repatriates in the IT sector requires immediate attention due to the rapid rise of India as a global market. To survive in an environment of cut-throat competition, organizations are required to have a proper understanding of the reasons for repatriate turnover in the IT sector and of the solutions to overcome this problem. Factors such as repatriate adjustment, job satisfaction, and organizational commitment are the key to understand the intentions for repatriate turnover. The primary objective of this study was to compare the role of organizational commitment as a mediator and as an independent variable for explaining the intent to leave. Feedback from IT repatriates in Indian organizations were generated. The items of the questionnaires were adapted from various previous studies to generate the relevant data. Data was generated from a sample of 273 respondents. Appropriate tools were employed for the purpose of refining and analysis of data. Study findings supported that organizational commitment role as a mediator is more important than as an independent variable in explaining the intentions of repatriate regarding the turnover. The study will provide useful insights to the practitioners and policymakers in understanding the process of repatriate adjustment and, hence, can assist in reducing the turnover intentions among the repatriates. Indian IT sector faces considerable high attrition. It has been observed that employees after returning from their foreign assignments find it difficult to readjust and thereafter eventually quit the organization. The turnover intention of repatriation in the IT sector requires immediate attention to arrest this kind of attrition. There is a paucity of studies focusing on turnover intention of repatriates in the Indian context. This study aims to fill that gap.

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This article is dedicated to the analysis of the cut flower market in Russia and identifying the main issues in this segment. It discusses the theoretical and methodological foundations of the market’s operation, as well as economic, technological, social, and environmental problems faced by the industry. In the context of the geopolitical crisis and harsh sanctions policy of Western countries, the purchase of flowers has become less relevant for the population. At the same time, Russians could not completely abandon the purchase of bouquets as a universal gift for any holiday, but taking into account the rise in prices, there was a reduction in the number of flowers in a bouquet, and, consequently, the overall demand for cut flowers in quantitative terms fell. In April 2022, the EU countries established a ban on exports of fresh cut flowers to Russia, and even despite the desire of the vast majority of suppliers to continue cooperation with Russian companies. Against the background of the geopolitical crisis, restrictions on currency transactions were introduced, SWIFT was disabled, which excluded the possibility of paying for goods directly. The search for intermediaries in other countries delayed delivery, in addition, many suppliers and intermediaries requested full prepayment, refused to return defective products and did not provide guarantees on the fact and timing of delivery due to the imbalance of logistics chains. At the same time, the maximum transportation time for flowers should take no more than 10 days, and such delays, coupled with longer land routes, had a negative impact on the freshness and quality of the flower. By mid-2022, supplies were generally in place, but all the commitments and risks that importers were taking on were pushing up prices for end consumers. Thus began the massive development of Russian agricultural holdings and greenhouses specializing in flowers.

Corporate sustainability reporting (CSR) is an essential component of corporate governance. This study examines the effect of corporate sustainability reporting on firm performance. The research examines the quality of disclosures for corporate sustainability practices and their implications for operating, financial, and market performance, as well as the holding period return of the stocks. A scoring index is constructed based on the hierarchical awards announced for best corporate and sustainability reports, published jointly by the Institute of Chartered Accountants, Pakistan (ICAP) and the Institute of Cost and Management Accountants, Pakistan (ICMAP). Empirical evidence indicates that the quality of disclosures and reporting related to Corporate Sustainability Practices has an insignificant effect on corporate performance in the context of Pakistan. The evidence can be ascribed to determinants such as economic and political instability, lack of commitment by boards and top management team, limited stakeholder engagement and over-investment in ESG initiatives. To effectively navigate the unique challenges associated with Pakistan, development of tailor-made indigenous framework is required, to facilitate corporate transparency in the form of CSR / ESG initiatives’ reporting that can improve firm valuation.

The burgeoning situation of online startups lives on from the dynamic skills of social media marketing (SMM). This indepth study addresses the transformational power o f social media platforms and explores ways to drive the success of online businesses. Through meticulo us reviews of relevant literature and compelling cas e studies, the report uncovers how strategic SMM s tartups can enable key goals. Key findings shed light on how social media can p romote brand awareness, foster a loyal customer ba se and promote inexpensive market penetration. Thi s study uses keyword analysis to identify the social media platforms most relevant to a particular target group, maximizing reach and commitment. Content marketing strategies are being considered to tackle content saturation, and social media analytics is bei ng discussed as a way to measure return on investm ent (ROI) and optimize campaigns. This report crea tes synergies between the growth of social media a nd online startups, providing entrepreneurs and mar keters with implementable knowledge to control th e ever developing digital ecosystem. Startups with knowle dge of best practices and influencer marketing tacti cs can use social media to build brand loyalty, mov e forward and achieve sustainable growth.

After completion of this case study, students/managers will be able to understand the concept of environmental sustainability and the role of traditional Indian farming practices in achieving environmental sustainability; analyse the role of environmentalism in shaping sustainable business practices within the context of Deccan Development Society (DDS); identify the challenges before DDS and explore the strategies that it can adopt towards sustainable expansion of the initiative; and propose actionable strategies for DDS to enhance its market reach while maintaining its commitment to sustainability. Women farmers of the Sangham of DDS gathered to discuss a recent initiative connecting conscious urban consumers with organic farmers. Under this model, consumers provided financial support before cultivation and farmers pledged their produce in return. This initiative benefited both parties – farmers received upfront financial aid, while consumers secured organic food – turning consumers into active contributors in the agricultural value chain. The initiative was a step in the right direction; however, farmers discussed about the issues and challenges pertaining to changing market dynamics. Mahadevamma, a women farmer who has been closely associated with DDS for a long time, along with other women farmers at DDS, was aware challenges. Amidst the discussion, Mahadevamma continued to contemplate, “in response to changing dynamics, expanding our reach and scaling up could strengthen our market presence, it will help us meet increasing demand, and it will also help many women famers; but success depends on improving logistics, branding, and strategic partnerships. Can this paper achieve it?”. This case has been developed for an Master of Business Administration core course on marketing management to teach the concepts related to sustainable marketing. More specifically, this case pertains to initiatives and actions towards sustainable marketing. This case, however, can be used in other core and elective courses, including business ethics and sustainability, agricultural marketing and rural marketing, among others. Teaching notes are available for educators only. CSS8: Marketing

This study examines the bidirectional relationship between environmental, social and governance (ESG) performance and firm performance in the global pharmaceutical sector, employing a cross-lagged panel model for 255 firms from 2016 to 2022. Using Bloomberg ESG scores and three performance metrics, namely return on assets (RoA), return on invested capital (RoIC) and Tobin’s Q, we uncover nuanced, non-linear relationships. Results indicate that lagged ESG performance positively influences accounting-based measures (RoA and RoIC), supporting the notion that sustainability investments yield operational efficiencies over time. However, market-based valuation (Tobin’s Q) exhibits a U-shaped relationship, suggesting initial investor scepticism followed by long-term rewards for persistent ESG commitment. On the other hand, financial performance does not consistently drive subsequent ESG improvements, rejecting reverse causality. The inverted U-shaped relationship for RoIC aligns with the environmental Kuznets curve (EKC), revealing diminishing returns to ESG investments beyond a threshold. These findings contribute to stakeholder and signalling theories, demonstrating that ESG integration enhances financial resilience while highlighting market inefficiencies in pricing sustainability. The study advances the ESG–performance literature by providing sector-specific insights, emphasizing the strategic value of ESG in pharmaceuticals, where regulatory and reputational stakes are high. Policymakers and managers can leverage these insights to optimize ESG resource allocation and improve disclosure frameworks.

Within a Closed-loop Supply Chain (CLSC) framework we study several consumer return behaviors for the used products which are based on the product prices and rebates. Consumers evaluate the rebate they receive as well as the price of the new product before deciding whether to dump a return. Therefore, the number of used products returned is examined under two types of rebates: a fixed rebate and a variable rebate. We search for the optimal rebate mechanism and find that the CLSC profits are higher under an variable rebate policy. This finding justifies the industry practices that employ a rebate mechanism based on both the value and the price of used item. We offer two types of solution concepts to the CLSC games: open-loop Stackelberg solution and Markov perfect Stackelberg solution, which are commonly employed in the dynamic games literature. While we mainly employ Markovian equilibrium, we also allow firms to utilize open-loop strategies so as to assess the impact of precommitment on the market outcomes. Therefore, we offer a comprehensive analysis of all possible market equilibrium solutions under different strategic considerations and the commitment deliberations. We show that under the fixed rebate regime open-loop solution coincides with Markov perfect solution. Furthermore, we show how consumer return behavior impacts the dynamic nature of the game. We find that the time frame is irrelevant if firms offer a fixed rebate. In contrast, the game will be fully dynamic when firms offer a variable rebate.

Effective patent value assessment provides decision support for patent transection and promotes the practical application of patent technology. The limitations of previous research on patent value assessment were analyzed in this work, and a wrapper-mode feature selection algorithm that is based on classifier prediction accuracy was developed. Verification experiments on multiple UCI standard datasets indicated that the algorithm effectively reduced the size of the feature set and significantly enhanced the prediction accuracy of the classifier. When the algorithm was utilized to establish an indicator system of patent value assessment, the size of the system was reduced, and the generalization performance of the classifier was enhanced. Sequential forward selection was applied to further reduce the size of the indicator set and generate an optimal indicator system of patent value assessment.

Recent advances in Pretrained Language Models (PLMs) and Large Language Models (LLMs) have demonstrated transformative capabilities across diverse domains. The field of patent analysis and innovation is not an exception, where natural language processing (NLP) techniques presents opportunities to streamline and enhance important tasks -- such as patent classification and patent retrieval -- in the patent cycle. This not only accelerates the efficiency of patent researchers and applicants, but also opens new avenues for technological innovation and discovery. Our survey provides a comprehensive summary of recent NLP-based methods -- including multimodal ones -- in patent analysis. We also introduce a novel taxonomy for categorization based on tasks in the patent life cycle, as well as the specifics of the methods. This interdisciplinary survey aims to serve as a comprehensive resource for researchers and practitioners who work at the intersection of NLP, Multimodal AI, and patent analysis, as well as patent offices to build efficient patent systems.

A key capability in managing patent applications or a patent portfolio is comparing claims to other text, e.g. a patent specification. Because the language of claims is different from language used elsewhere in the patent application or in non-patent text, this has been challenging for computer based natural language processing. We test two new LLM-based approaches and find that both provide substantially better performance than previously published values. The ability to match dense information from one domain against much more distributed information expressed in a different vocabulary may also be useful beyond the intellectual property space.

Effective query formulation is a key challenge in long-document Information Retrieval (IR). This challenge is particularly acute in domain-specific contexts like patent retrieval, where documents are lengthy, linguistically complex, and encompass multiple interrelated technical topics. In this work, we present the application of recent extractive and abstractive summarization methods for generating concise, purpose-specific summaries of patent documents. We further assess the utility of these automatically generated summaries as surrogate queries across three benchmark patent datasets and compare their retrieval performance against conventional approaches that use entire patent sections. Experimental results show that summarization-based queries significantly improve prior-art retrieval effectiveness, highlighting their potential as an efficient alternative to traditional query formulation techniques.

Generative language models are promising for assisting human writing in various domains. This manuscript aims to build generative language models in the patent domain and evaluate model performance from a human-centric perspective. The perspective is to measure the ratio of keystrokes that can be saved by autocompletion based on generative patent language models. A higher ratio means a more effective model which can save more keystrokes. This metric can be used to benchmark model performance. The metric is different from conventional machine-centric metrics that are token-based instead of keystroke-based. In terms of model size, the largest model built in this manuscript is 6B, which is state-of-the-art in the patent domain. Based on the metric, it is found that the largest model is not necessarily the best for the human-centric metric. The finding means that keeping increasing model sizes in the patent domain might be unnecessary if the purpose is to assist human writing with autocompletion. Several patent language models are pre-trained from scratch in this research. The pre-trained models are released for future researchers. Several visualization tools are also provided. The importance of building a generative language model in the patent domain is the potential to facilitate creativity and innovations in the future.

An investor is estimating net present value of a firm project and performs risk analysis. Usually it is created portfolio hierarchies and make comparison of variants of project based on these hierarchies. Then one finds that portfolio which corresponds to the particular needs of individual groups within the firm. We have formulated a new type of NPV analysis based on the fact that normal distribution of NPV is observed for some projects in some industries. The expected risk of the project is given by variance, in which there is the standard deviation of the year n cash flow, the standard deviation of the investment I in the time zero, the correlation coefficient of the year n cash flow deviation from the average and of the investment I at time zero deviation from the mean investment at time zero, the correlation coefficient of the year n cash flow deviation from the average and of the year n' cash flow deviation from the average. The aim function of the investor into the project was found. The investor is characterized by the constant A. The larger constant A the larger preference is given to the project NPV and the larger acceptable risk of the project, and vice versa. We have found that there are contributions in which we have the aim-function-like contribution to the aim function, which is discounted and in which the risk of the n-th year risk is discounted in the second order. Further there is aim-function-like contribution to the aim-function which comes from the initial investment I and its risk.

In this paper, we propose a novel method for the prior-art search task. We fine-tune SciBERT transformer model using Triplet Network approach, allowing us to represent each patent with a fixed-size vector. This also enables us to conduct efficient vector similarity computations to rank patents in query time. In our experiments, we show that our proposed method outperforms baseline methods.

Patent images are technical drawings that convey information about a patent's innovation. Patent image retrieval systems aim to search in vast collections and retrieve the most relevant images. Despite recent advances in information retrieval, patent images still pose significant challenges due to their technical intricacies and complex semantic information, requiring efficient fine-tuning for domain adaptation. Current methods neglect patents' hierarchical relationships, such as those defined by the Locarno International Classification (LIC) system, which groups broad categories (e.g., "furnishing") into subclasses (e.g., "seats" and "beds") and further into specific patent designs. In this work, we introduce a hierarchical multi-positive contrastive loss that leverages the LIC's taxonomy to induce such relations in the retrieval process. Our approach assigns multiple positive pairs to each patent image within a batch, with varying similarity scores based on the hierarchical taxonomy. Our experimental analysis with various vision and multimodal models on the DeepPatent2 dataset shows that the proposed method enhances the retrieval results. Notably, our method is effective with low-parameter models, which require fewer computational resources and can be deployed on environments with limited hardware.

Firms' decisions to patent innovations involve a complex evaluation of costs, benefits, and strategic considerations. This article explores the economic and practical factors that influence whether companies seek patent protection. It discusses explicit monetary costs--such as legal fees, filing, and international expenses--and non-monetary costs related to public disclosure. Benefits include the provision of exclusion rights that secure returns and protect competitive advantages. Additionally, firms use patents as strategic tools to block rivals, strengthen bargaining positions, signal innovation quality, and attract investment. The analysis also considers alternative protection strategies like secrecy, particularly for small enterprises or industries where disclosure is detrimental.

Understanding firm conduct is crucial for industrial organization and antitrust policy. In this article, we develop a testing procedure based on the Rivers and Vuong non-nested model selection framework. Unlike existing methods that require estimating the demand and supply system, our approach compares the model fit of two first-stage price regressions. Through an extensive Monte Carlo study, we demonstrate that our test performs comparably to, or outperforms, existing methods in detecting collusion across various collusive scenarios. The results are robust to model misspecification, alternative functional forms for instruments, and data limitations. By simplifying the diagnosis of firm behavior, our method offers researchers and regulators an efficient tool for assessing industry conduct under a Bertrand oligopoly framework. Additionally, our approach offers a practical guideline for enhancing the strength of BLP-style instruments in demand estimation: once collusion is detected, researchers are advised to incorporate the product characteristics of colluding partners into own-firm instruments while excluding them from other-firm instruments.

This study investigates the relationship between innovation activities and firm-level productivity among early-stage high-tech startups in China. Using a proprietary dataset encompassing patent records, R&D expenditures, capital valuation, and firm performance from 2020 to 2024, we examine whether and how innovation, measured by patents and R&D input, translates into economic output. Contrary to established literature, we find that patent output does not significantly contribute to either income or profit among the sampled firms. Further investigation reveals that patents may primarily serve a signaling function to external investors and policymakers, rather than reflecting true innovative productivity. In contrast, R&D expenditure shows a consistent and positive association with firm performance. Through mechanism analysis, we explore three channels (organizational environment, employee quality, and policy-driven incentives) to explain the impact of R&D, identifying capital inflow and valuation as key drivers of R&D investment. Finally, heterogeneity analysis indicates that the effects of R&D are more pronounced in sub-industries such as smart terminals and digital creativity, and for firms based in Shenzhen. Our findings challenge the prevailing assumption that patent output is a universal indicator of innovation success and underscore the context-dependent nature of innovation-performance linkages in emerging markets.

This study investigates the causal relationship between patent grants and firms' dynamics in the Information and Communication Technology (ICT) industry, as the latter is a peculiar sector of modern economies, often under the lens of antitrust authorities. For our purpose, we exploit matched information about financial accounts and patenting activity in 2009-2017 by 179,660 companies operating in 39 countries. Preliminarily, we show how bigger companies are less than 2% of the sample, although they concentrate about 89% of the grants obtained in the period of analyses. Thus, we test that patent grants in the ICT industry have a significant and large impact on market shares and firm size of smaller companies (31.5% and 30.7%, respectively) in the first year after the grants, while we have no evidence of an impact for bigger companies. After a novel instrumental variable strategy that exploits information at the level of patent offices, we confirm that most of the effects on smaller companies are due to the protection of property rights and not to the innovative content of inventions. Finally, we never observe a significant impact on either profitability or productivity for any firm size category. Eventually, we discuss how our findings support the idea that the ICT industry is a case of endogenous R&D sunk costs, which prevent profit margins from rising in the presence of a relatively high market concentration.

Where do firms innovate? Mapping their locations and directions in technological space is challenging due to its high dimensionality. We propose a new method to characterize firms' inventive activities via topological data analysis (TDA) that represents high-dimensional data in a shape graph. Applying this method to 333 major firms' patents in 1976--2005 reveals substantial heterogeneity: some firms remain undifferentiated; others develop unique portfolios. Firms with unique trajectories, which we define and measure graph-theoretically as "flares" in the Mapper graph, perform better. This association is statistically and economically significant, and continues to hold after we control for portfolio size, firm survivorship, industry classification, and firm fixed effects. By contrast, existing techniques -- such as principal component analysis (PCA) and Jaffe's (1989) clustering method -- struggle to track these firm-level dynamics.

We introduce the notion of firm non-expansive mapping in weak metric spaces, extending previous work for Banach spaces and certain geodesic spaces. We prove that, for firm non-expansive mappings, the minimal displacement, the linear rate of escape, and the asymptotic step size are all equal. This generalises a theorem by Reich and Shafrir.

Generative models, such as GPT-2, have demonstrated impressive results recently. A fundamental question we'd like to address is: where did the generated text come from? This work is our initial effort toward answering the question by using prior art search. The purpose of the prior art search is to find the most similar prior text in the training data of GPT-2. We take a reranking approach and apply it to the patent domain. Specifically, we pre-train GPT-2 models from scratch by using the patent data from the USPTO. The input for the prior art search is the patent text generated by the GPT-2 model. We also pre-trained BERT models from scratch for converting patent text to embeddings. The steps of reranking are: (1) search the most similar text in the training data of GPT-2 by taking a bag-of-word ranking approach (BM25), (2) convert the search results in text format to BERT embeddings, and (3) provide the final result by ranking the BERT embeddings based on their similarities with the patent text generated by GPT-2. The experiments in this work show that such reranking is better than ranking with embeddings alone. However, our mixed results also indicate that calculating the semantic similarities among long text spans is still challenging. To our knowledge, this work is the first to implement a reranking system to identify retrospectively the most similar inputs to a GPT model based on its output.

While large language models (LLMs) excel at factual recall, the real challenge lies in knowledge application. A gap persists between their ability to answer complex questions and their effectiveness in performing tasks that require that knowledge. We investigate this gap using a patent classification problem that requires deep conceptual understanding to distinguish semantically similar but objectively different patents written in dense, strategic technical language. We find that LLMs often struggle with this distinction. To diagnose the source of these failures, we introduce a framework that decomposes model errors into two categories: missing knowledge and unused knowledge. Our method prompts models to generate clarifying questions and compares three settings -- raw performance, self-answered questions that activate internal knowledge, and externally provided answers that supply missing knowledge (if any). We show that most errors stem from failures to deploy existing knowledge rather than from true knowledge gaps. We also examine how models differ in constructing task-specific question-answer databases. Smaller models tend to generate simpler questions that they, and other models, can retrieve and use effectively, whereas larger models produce more complex questions that are less effective, suggesting complementary strengths across model scales. Together, our findings highlight that shifting evaluation from static fact recall to dynamic knowledge application offers a more informative view of model capabilities.

In this work, we attempt to provide a comprehensive granular account of the pace of technological change. More specifically, we survey estimated yearly performance improvement rates for nearly all definable technologies for the first time. We do this by creating a correspondence of all patents within the US patent system to a set of technology domains. A technology domain is a body of patented inventions achieving the same technological function using the same knowledge and scientific principles. We obtain a set of 1757 domains using an extension of the previously defined classification overlap method (COM). These domains contain 97.14% of all patents within the entire US patent system. From the identified patent sets, we calculated the average centrality of the patents in each domain to estimate their improvement rates, following a methodology tested in prior work. The estimated improvement rates vary from a low of 1.9% per year for the Mechanical Skin treatment - Hair Removal and wrinkles domain to a high of 228.8% per year for the Network management - client-server applications domain. We developed a one-line descriptor identifying the technological function achieved and the underlying knowledge base for the largest 50, fastest 20 as well as slowest 20 of these domains, which cover more than forty percent of the patent system. In general, the rates of improvement were not a strong function of the patent set size and the fastest improving domains are predominantly software-based. We make available an online system that allows for automated searching for domains and improvement rates corresponding to any technology of interest to researchers, strategists and policy formulators.

While patents and standards have been identified as essential driving components of innovation and market growth, the inclusion of a patent in a standard poses many difficulties. These difficulties arise from the contradicting natures of patents and standards, which makes their combination really challenging, but, also, from the opposing business and market strategies of different patent owners involved in the standardisation process. However, a varying set of policies has been adopted to address the issues occurring from the unavoidable inclusion of patents in standards concerning certain industry sectors with a constant high degree of innovation, such as telecommunications. As these policies have not always proven adequate enough, constant efforts are being made to improve and expand them. The intriguing and complicated relationship between patents and standards is finally examined through a review of the use cases of well-known standards of the telecommunications sector which include a growing set of essential patents.

This study assesses the degree to which the social value of patents can be connected to the private value of patents across discrete and complex innovation. The underlying theory suggests that the social value of cumulative patents is less related to the private value of patents. We use the patents applied between 1995 to 2002 and granted on or before December 2018 from the Indian Patent Office (IPO). Here the patent renewal information is utilized as a proxy for the private value of the patent. We have used a variety of logit regression model for the impact assessment analysis. The results reveal that the technology classification (i.e., discrete versus complex innovations) plays an important role in patent value assessment, and some technologies are significantly different than the others even within the two broader classifications. Moreover, the non-resident patents in India are more likely to have a higher value than the resident patents. According to the conclusions of this study, only a few technologies from the discrete and complex innovation categories have some private value. There is no evidence that patent social value indicators are less useful in complicated technical classes than in discrete ones.

This work proposes to measure the scope of a patent claim as the reciprocal of self-information contained in this claim. Self-information is calculated based on a probability of occurrence of the claim, where this probability is obtained from a language model. Grounded in information theory, this approach is based on the assumption that an unlikely concept is more informative than a usual concept, insofar as it is more surprising. In turn, the more surprising the information required to define the claim, the narrower its scope. Seven language models are considered, ranging from simplest models (each word or character has an identical probability) to intermediate models (based on average word or character frequencies), to large language models (LLMs) such as GPT2 and davinci-002. Remarkably, when using the simplest language models to compute the probabilities, the scope becomes proportional to the reciprocal of the number of words or characters involved in the claim, a metric already used in previous works. Application is made to multiple series of patent claims directed to distinct inventions, where each series consists of claims devised to have a gradually decreasing scope. The performance of the language models is then assessed through several ad hoc tests. The LLMs outperform models based on word and character frequencies, which themselves outdo the simplest models based on word or character counts. Interestingly, however, the character count appears to be a more reliable indicator than the word count.

This paper introduces Natural Language Processing for identifying ``true'' green patents from official supporting documents. We start our training on about 12.4 million patents that had been classified as green from previous literature. Thus, we train a simple neural network to enlarge a baseline dictionary through vector representations of expressions related to environmental technologies. After testing, we find that ``true'' green patents represent about 20\% of the total of patents classified as green from previous literature. We show heterogeneity by technological classes, and then check that `true' green patents are about 1\% less cited by following inventions. In the second part of the paper, we test the relationship between patenting and a dashboard of firm-level financial accounts in the European Union. After controlling for reverse causality, we show that holding at least one ``true'' green patent raises sales, market shares, and productivity. If we restrict the analysis to high-novelty ``true'' green patents, we find that they also yield higher profits. Our findings underscore the importance of using text analyses to gauge finer-grained patent classifications that are useful for policymaking in different domains.

Patents are legal documents that aim at protecting inventions on the one hand and at making technical knowledge circulate on the other. Their complex style -- a mix of legal, technical, and extremely vague language -- makes their content hard to access for humans and machines and poses substantial challenges to the information retrieval community. This paper proposes an approach to automatically simplify patent text through rephrasing. Since no in-domain parallel simplification data exist, we propose a method to automatically generate a large-scale silver standard for patent sentences. To obtain candidates, we use a general-domain paraphrasing system; however, the process is error-prone and difficult to control. Thus, we pair it with proper filters and construct a cleaner corpus that can successfully be used to train a simplification system. Human evaluation of the synthetic silver corpus shows that it is considered grammatical, adequate, and contains simple sentences.

Existing research often assumes that firms’ financial reporting choices influence their return comovement with other firms. We examine the validity of that assumption. First, we provide initial evidence suggesting that similarity in two firms’ disclosures not only predicts but influences future return comovement between those two firms. Second, we show that this predictive ability aggregates to the market level; disclosure similarity can be used to estimate more accurate forward-looking market betas. Taken together, these two results suggest that firms’ reporting decisions can influence their firms’ betas even in the absence of changes to capital structure or operations. This paper was accepted by Suraj Srinivasan, accounting. Funding: The authors are grateful for financial support from the Brigham Young University Marriott School of Business and the Fisher College of Business, Ohio State University. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4915 .

This study aims to examine the effect between internal capital disclosure (ICD) and stock return with the mediation of return on asset (ROA) on the banking sector of Southeast Asia. We find that ICD does not have direct correlation with stock returns, ICD has a positive and significant effect with ROA, ROA has a significant and positive effect with stock return, and the mediating variable ROA can mediate effect between ICD on stock return. Bounded rationality or cognitive limitation resulted in investors needing mediation that ease capturing, memorizing, and processing of information in their minds, one of which is to use return on asset as a bridge between internal capital disclosure and stock return. Aside from that, for investors it is very possible to gain big advantage if they can analyze ICD texts and do trading strategy adjustments, because this study stated that there is a positive effect between ICD and ROA that impacts stock return.